2010/12/31 by Paulo Urriza, Eric Rebeiz, Przemysław Pawełczak +1
Agricultural and Biological Sciences · Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · #Additive white Gaussian noise #Algorithm #Artificial intelligence #Computational complexity theory #Computer science #Cumulant #Cumulative distribution function #Fractal and DNA sequence analysis #Genetic and Environmental Crop Studies #Jitter #Mathematics #Modulation (music) #Pattern recognition (psychology) #Physics #Probability density function #Probability distribution #Statistics #Telecommunications #White noise #Wireless Signal Modulation Classification #cs.IT #cs.PF #math.IT #stat.ML
paper · pdf · doi:10.1109/lcomm.2011.032811.110316
3 pages, resubmitted to IEEE Communication Letters (modified based on reviewer comments)
arxiv created 2011/02/19 · openalex publication_date 2011/04/06 · arxiv updated 2011/09/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
We present a novel modulation level classification (MLC) method based on probability distribution distance functions. The proposed method uses modified Kuiper and Kolmogorov-Smirnov distances to achieve low computational complexity and outperforms the state of the art methods based on cumulants and goodness-of-fit tests. We derive the theoretical performance of the proposed MLC method and verify it via simulations. The best classification accuracy, under AWGN with SNR mismatch and phase jitter, is achieved with the proposed MLC method using Kuiper distances.